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A Comparative Risk Assessment Methodology for Prioritizing Risk Management Policy Initiatives: Ranking of Industrial Waste Streams in Portugal

Authors:
  • Vireo Advisors

Abstract

Effective environmental risk management strategies increasingly apply risk-based methods for priority setting. This paper presents a comparative risk assessment of industrial waste streams in Portugal, where such a prioritization is even more important due to a missing integrative framework to address the industrial waste disposal. The ranking represents a prioritization of waste types and geographical locations based on potential risk to health and environment. Alternatives are compared for public health, environmental, and other risk attributes, providing managers with transparent decision criteria and a semi-quantitative risk-based foundation for decision making. The methodology developed requires limited analytical effort and is an inexpensive alternative to large detailed studies for setting priorities based on risk in the early stages of environmental planning. By developing probability distributions, an uncertainty analysis adds weight to the findings and highlights areas where better data is useful The methodology is applied to industries in Portugal that are known to produce and dispose of hazardous wastes. Industries are ranked in a semi-quantitative process on five scales, by the human and ecological toxicity of the hazardous waste components, their bioaccumulation potential, environmental persistence, and finally by the annual quantity of disposed hazardous waste. Further, geographical areas are compared for the most significant potential environmental impacts based on area specific exposure factors
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A possible approach for determining soil and groundwater quality criteria for contaminated sites is the comparative risk assessment. Originating from but not limited to Italian interest in a decentralised (regional) implementation of comparative risk assessment, this paper first addresses the proposal of an original methodology called CORIANREG-M, which was created with initial attention to the context of potentially contaminated sites in the Marche Region (Central Italy). To deepen the technical-scientific knowledge and applicability of the comparative risk assessment, the following characteristics of the CORIANREG-M methodology appear to be relevant: the simplified but logical assumption of three categories of factors (source and transfer/transport of potential contamination, and impacted receptors) within each exposure pathway; the adaptation to quality and quantity of data that are available or derivable at the given scale of concern; the attention to a reliable but unsophisticated modelling; the achievement of a conceptual linkage to the absolute risk assessment approach; and the potential for easy updating and/or refining of the methodology. Further, the application of the CORIANREG-M methodology to some case-study sites located in the Marche Region indicated the following: a positive correlation can be expected between air and direct contact pathway scores, as well as between individual pathway scores and the overall site scores based on a root-mean-square algorithm; the exposure pathway, which presents the highest variability of scores, tends to be dominant at sites with the highest computed overall site scores; and the adoption of a root-mean-square algorithm can be expected to emphasise the overall site scoring.
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A ranking system for contaminated sites based on comparative risk methodology using fuzzy Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) was developed in this article. It combines the concepts of fuzzy sets to represent uncertain site information with the PROMETHEE, a subgroup of Multi-Criteria Decision Making (MCDM) methods. Criteria are identified based on a combination of the attributes (toxicity, exposure, and receptors) associated with the potential human health and ecological risks posed by contaminated sites, chemical properties, site geology and hydrogeology and contaminant transport phenomena. Original site data are directly used avoiding the subjective assignment of scores to site attributes. When the input data are numeric and crisp the PROMETHEE method can be used. The Fuzzy PROMETHEE method is preferred when substantial uncertainties and subjectivities exist in site information. The PROMETHEE and fuzzy PROMETHEE methods are both used in this research to compare the sites. The case study shows that this methodology provides reasonable results.
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Critics of comparative risk assessment (CRA), the increasingly common practice of juxtaposing disparate risks for the purpose of declaring which one is the "larger" or the "more important," have long focused their concern on the difficulties in accommodating the qualitative differences among risks. To be sure, people may disagree vehemently about whether "larger" necessarily implies "more serious," but the attention to this aspect of CRA presupposes that science can in fact discern which of two risks has the larger statistical magnitude. This assumption, encouraged by the indiscriminate calculation of risk ratios using arbitrary point estimates, is often incorrect: the fact that environmental and health risks differ in unknown quantitative respects is at least as important a caution to CRA as the fact that risks differ in known qualitative ways. To show how misleading CRA can be when uncertainty is ignored, this article revisits the claim that aflatoxin contamination of peanut butter was "18 times worse" than Alar contamination of apple juice. Using Monte Carlo simulation, the number 18 is shown to lie within a distribution of plausible risk ratios that ranges from nearly 400:1 in favor of aflatoxin to nearly 40:1 in the opposite direction. The analysis also shows that the "best estimates" of the relative risk of aflatoxin to Alar are much closer to 1:1 than to 18:1. The implications of these findings for risk communication and individual and societal decision-making are discussed, with an eye toward improving the general practice of CRA while acknowledging that its outputs are uncertain, rather than abandoning it for the wrong reasons. Images Figure 1. Figure 2.
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